Sciweavers

Share
PAMI
2012

Domain Transfer Multiple Kernel Learning

6 years 8 months ago
Domain Transfer Multiple Kernel Learning
—Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. To cope with the considerable change between feature distributions of different domains, we propose a new cross-domain kernel learning framework into which many existing kernel methods can be readily incorporated. Our framework, referred to as Domain Transfer Multiple Kernel Learning (DTMKL), simultaneously learns a kernel function and a robust classifier by minimizing both the structural risk functional and the distribution mismatch between the labeled and unlabeled samples from the auxiliary and target domains. Under the DTMKL framework, we also propose two novel methods by using SVM and prelearned classifiers, respectively. Comprehensive experiments on three domain adaptation data sets (i.e., TRECVID, 20 Newsgroups, and email spam data sets) demonstrate that DTMKL-...
Lixin Duan, Ivor W. Tsang, Dong Xu
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where PAMI
Authors Lixin Duan, Ivor W. Tsang, Dong Xu
Comments (0)
books